Automatically assessing and optimizing the quality of end user service sessions being carried on a network is a requirement of modern network operators. It is a challenging task because of the myriad of application session types now extant in networks and because of the sheer volume of end user sessions in progress at any one time. It is necessary to monitor and understand a vast amount of disparate data in order to deduce the quality that is being experienced on sessions and to suggest optimizations to a network that might improve the quality experience. The service session delivery context as well as the raw service metrics must be understood in order to get a full picture of the service quality.
Current information modelling practice is to model information statically, typically using Unified Modelling Language (UML) models or more primitive XML schemas to describe the structure of information. The meaning of a certain type of information and its relationships with other types of information is not captured in such models; linking and adding meaning to data is carried out using adapters, mappers, and translators implemented as computer programs.
Much work has been carried out in the field of semantic modelling where the structure, meaning, and references of models are captured using technologies such as Resource Description Framework (RDF) graphs and ontologies written in languages such as Ontology Web Language (OWL). Such models can themselves be executed, without the need for adapters, mappers, and translators.
To date, semantic approaches have only been suggested in very limited applications in the field of Network Management, for example, as disclosed by M. Serrano, J. Strassner, and M. Foghlu, “A Formal Approach for the Inference Plane Supporting Integrated Management Tasks in the Future Internet”, Integrated Network Management-Workshops, 2009. IM '09, IFIP/IEEE International Symposium on, pages 120-127, June 2009; N. Sheridan-Smith, T. O'Neill, J. Leaney, and M. Hunter, “A Policy-based Service Definition Language for Service Management”, Network Operations and Management Symposium, 2006. NOMS 2006. 10th IEEE/IFIP, pages 282-293, April 2006, and H. Muñoz Frutos, I. Kotsiopoulos, L. Vaquero Gonzalez, and L. Rodero Merino, “Enhancing Service Selection by Semantic QoS”, L. Aroyo, P. Traverso, F. Ciravegna, P. Cimiano, T. Heath, E. Hyvönen, R. Mizoguchi, E. Oren, M. Sabou, and E. Simperl, editors, The Semantic Web: Research and Applications, volume 5554 of Lecture Notes in Computer Science, pages 565-577, Springer Berlin/Heidelberg, 2009. This limited application is partly due to the application of reasoning, querying, and rules to large semantic models is a performance challenge, see for example, Keeney, J. and Boran, A. and Bedini, I. and Matheus, C. J. and Patel-Schneider, P. F. “Approaches to Relating and Integrating Semantic Data from Heterogeneous Sources”, Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011 IEEE/WIC/ACM International Conference on, volume 1, pages 170-177, August. 2011; and J. López de Vergara, A. Guerrero, V. Villagrá, and J. Berrocal, “Ontology-Based Network Management: Study Cases and Lessons Learned”, Journal of Network and Systems Management, 17:234-254, 2009.
One existing solution is to use Ipoque (www.ipoque.com) which supports the list of protocols and applications. The packet inspection library of such a system runs to 4 pages of tightly packed text, i.e. a large amount of application protocols exist. Understanding each application and tuning a network to give the best balance of performance across the set of services currently running in the network would be difficult with such a large amount of protocols.
Automated service analysis and optimization may consider the service expectations for a set of services at the service consumption point. These may be set, agreed and actively managed. Once these expectations have been agreed, the actual service experience and service context that users are experiencing is monitored. The service context may be adjusted to optimize service delivery. It is desirable that any system that addresses these challenges is adaptable, highly flexible, and operates with minimal human intervention.
Service expectations are a balance struck between quality, cost, and resource efficiency for the delivery of services. Contracts such as Service Level Agreements (SLAs) are often used to express service expectations where service delivery crosses organizational boundaries. Service expectations are not formally set with service providers such as video streaming service that utilise pure Internet connectivity. End users usually subscribe for a certain bandwidth level or bundle that implicitly sets a service expectation. The service experience is the quality of service (QoS) of a service measured at the point of delivery and the quality of experience (QoE) of the service as perceived by the end user. Although the raw statistics that give QoS measurements for the quality of a service are important, those measurements must be viewed in the overall context of the service delivery to be properly understood. The service context is that set of factors which can affect service quality.
One example of a source of end user service experience and service context of existing systems is the use of counters to report network metrics. A vast number of counters are available, aggregating metrics such as packet loss, delay, and jitter as well as on network events such as equipment failures and overloads. Counters are also used to report on logical entities such as Virtual Local Area Networks (VLANs) and MultiProtocol Label Switching (MPLS) tunnels. Simple Network Management Protocol (SNMP) Management Information Bases (MIBs) and the 3GPP PM Integration Reference Point (IRP) are just two of very many standards that specify counters and counter handling.
Event-based metric collection, where network elements report metrics on significant events in bearer and control sessions, are increasingly being used to provide a rich source of data of end user service experience and service context. Event-based metrics may be collected from Evolved Universal mobile telecommunication system Terrestrial Radio Access Network (UTRAN) Node B (eNodeB) nodes, Serving GPRS Serving Node (SGSN) nodes, Gateway General packet radio system Support Node (GGSN), nodes and MME nodes. Internet Protocol Flow Information eXport (IPFIX) may be used to stream reports of events on (IP) flows from network elements.
Reporting directly from service terminals is one of the most accurate ways of evaluating end user service context. Service experience metrics available in terminal reports include QoS metrics such as packet loss and latency, and QoE estimations made by algorithms running in the terminal. Terminal reports also report context information such as the service user, the location of service delivery, and device information such as processor load, memory usage, disk space, and remaining energy.
Standardization activities for terminal reporting are specific to particular service or networking domains. Real Time Transport Control Protocol (RTCP) is used for quality reporting on Real Time Transport Protocol (RTP) based streaming services. The 3GPP use terminal reports to monitor Packet-switched Streaming Service (PSS), Multimedia Broadcast Multicast Service (MBMS) and Multimedia Telephony (MMTel) sessions. As there is no common standard for terminal reporting, Generic Service Quality Reporting Protocol (GSQR) has been proposed as a unified approach for terminal reporting as disclosed, for example, by WO2010/066288 and WO2012/084010.
Probes can be used to report events on signalling and bearer links at IP, Transport Control protocol (TCP), and application protocol level and can give very detailed information on application, session, and individual user level. Packet capture streams are intercepted by hardware probes and can be passed through analysers such as Ipoque to classify the packets. Probes necessitate the installation of specialized equipment in the network and a tap point to intercept packets.
Management systems such as billing systems and customer relationship management systems may also contain relevant contextual information on users and usage. They give information such as the type of accounts users have and the amount of money being spent by users. Network planning systems have information on the current and planned deployment of network equipment. Business management systems have information on the business strategies and rules for particular network services offered to customers.
End user services can be affected by weather and other natural phenomena. Sources of such contextual information can be used to explain why service degradation has occurred. For example, a storm can cause a transitory degradation in mobile network service that has a knock on effect of end user service degradation. However, there is no requirement to optimize the network in such a case because the storm will pass.
Other systems such as news systems or systems that give schedules of upcoming popular sporting events or concerts are also valuable sources of information. They can help explain abnormal concentrations of user activity, perhaps due to some celebrity related happening or due to a rock concert being held in a rural area.
Although there is a long history of modelling in the telecommunication domain, the focus has been largely on network equipment and resource models rather than on services being carried by networks. SNMP MIBs described in Structure of Management Information (SMI), the Distributed Management Task Force's Common Information Model (DMTF's CIM), and 3GPP IRP models such as the E-UTRAN Network Resource Model (NRM) are typical of such models.
Some work has been undertaken in modelling telecommunication services. One approach is to describe a generic telecommunication service model that identifies service relationships such as user, functionality, and QoS parameters. Another approach uses service template models to describe a service in terms of a generic service model. The TM-Forum's Shared Information/Data (SID) Model is a generic service model with relationships and mappings between service entities described conceptually as either a uses or a requires relationship.
All of the models and approaches above capture the static structure of the information, but do not capture the meaning of the models nor the linkages between the models in a way that allows the models be processed by machines.
There have been some attempts to build executable models for telecommunication services. SALmon is a domain specific language for service modelling, allowing specification of service model instances using programming language like specifications. The disadvantage of this approach is that all model specifications must be programmed. Another known approach is to model telecommunication services as policies. In Directory Enabled Networks-Next Generation (DEN-ng), services sit in the Business View of the Policy Continuum. The DEN-ng is a comprehensive unifying model that encompasses the entire domain of network management. The drawbacks of this model are that it is tightly integrated, assumes a policy management infrastructure is in place, and is not open.
Semantic models, known as ontologies, capture the structure and meaning of the concepts and relationships in a domain in a formal manner, with constraints and restrictions formally expressed. The natural language text and annotations often used on UML models to express complex relationships and restrictions can be written into ontologies as part of the model. Ontologies can have varying degrees of semantic richness; the amount and type of knowledge stored in the ontology. Simple ontologies such as taxonomies describe only the hierarchy of a set of concepts whereas more complex ontologies capture complex relationships as axioms and rules. Machines can process ontologies if they are properly written.
Resource Description Framework (RDF) descriptions are used to capture simple ontologies. In RDF, the concepts in a domain are modelled as nodes on a directed graph, with relationships between concepts represented as arcs between nodes. RDF can be shown graphically or in a number of notations including XML. RDF Schema (RDFS) extends RDF to allow concepts to be classed and properties of concepts to be expressed.
More complex ontologies are expressed in languages such as OWL, the Web Ontology Language. OWL is, in fact, a family of languages that have varying degrees of expressiveness. The flavours of OWL with high expressiveness have higher computational demands than those with lower expressiveness. OWL is a subset of RDF and is more expressive than RDF. It allows more complex class hierarchical relationships such as equivalence and unions to be expressed. It supports specification of restrictions and cardinalities on properties. Characteristics such as transitiveness and symmetry can be given to the relationships between concepts. Rules can be applied to the ontology using rule languages such as Semantic Web Rule Language (SWRL).
The strength of ontologies is that they can capture complex relationships across disparate knowledge domains and that knowledge can be captured in the model without the need for a large amount of programmed mappers, adapters, and translators.
A knowledge base holds the ontology definition and the actual knowledge of ontology. Once the structure, constraints, axioms, and rules have been specified, the ontology is populated with instances of concepts (individuals). A reasoner is used to infer knowledge in the knowledge base, as individuals are inserted into the ontology and the reasoner runs, working out the relationships between the concepts. The SPARQL Protocol and RDF Query Language (SPARQL) can be used to retrieve knowledge from the knowledge base.
Semantic models have the richness to describe the complex relationships that exist between the disparate models that describe the expectations, experience, and context of end user services. However, to date, little work has been undertaken in building such models.
The Ontology for Support and Management is disclosed by M. Serrano, J. Strassner, and M. Foghlu, “A Formal Approach for the Inference Plane Supporting Integrated Management Tasks in the Future Internet”, Integrated Network Management-Workshops, 2009. IM '09. IFIP/IEEE International Symposium on, pages 120-127, June 2009. This system captures changes to user, location, device, and service context as events that trigger policies associated with entities modelled in DEN-ng. The BREIN ontology disclosed by H. Muñoz Frutos, I. Kotsiopoulos, L. Vaquero Gonzalez, and L. Rodero Merino, “Enhancing Service Selection by Semantic QoS”, L. Aroyo, P. Traverso, F. Ciravegna, P. Cimiano, T. Heath, E. Hyvönen, R. Mizoguchi, E. Oren, M. Sabou, and E. Simperl, editors, The Semantic Web: Research and Applications, volume 5554 of Lecture Notes in Computer Science, pages 565-577. Springer Berlin/Heidelberg, 2009 is an OWL ontology that defines basic QoS concepts for connectivity services provided by telecommunication networks.
Of course, it is possible to use UML models as a basis for ontology design. In some cases, it is possible to translate well-specified UML models into an ontology described in OWL. Guillaume Hillairet, Zoltán Theisz, Epifanio Salamanca Cuadrado, and David Cleary did just that and translated the TM Forum's SID model into ontology in OWL. Of course, general-purpose ontologies can be used to model non-telecommunication aspects of end user service context.
Ontology mapping is used to map relationships between concepts across ontologies. Keeney et al. J. Keeney, D. Lewis, and D. O'Sullivan, “Ontological Semantics for Distributing Contextual Knowledge in Highly Distributed Autonomic Systems”, Journal of Network and Systems Management, 15:75-86, 2007 use ontology mappings in a Knowledge Based Network to enable semantic interoperability between producers and consumers of network information. The Knowledge Based Network allows producers to publish and consumers to subscribe to knowledge. Information models describing knowledge to be published into the system are analysed off line to produce deployable run time mappings. This means that information in any form, once mapped, can be published into or consumed from the system. Reasoning is used to determine which set of mappings should be used for a particular event forwarding operation.
Semantic lifting is used to translate information into a semantic form. Semantic Annotations for Web Services Description Language (SAWSDL) is used to annotate element definitions in XML schemas with semantic references and mappings that translate XML element information to semantic knowledge. SAWSDL is primarily used to annotate WSDL web service definitions. Lehtihet and Agoulmine E. Lehtihet and N. Agoulmine, “Towards integrating Management Interfaces”, Network Operations and Management Symposium, 2008. NOMS 2008. IEEE, pages 807-810, April 2008 describe an approach for reusing information from SMI and CIM models by mapping those models to a common UML structure, which is then transformed to XML schemas. Those XML schemas are then annotated with SAWSDL. Frutos et al. H. Munoz Frutos, I. Kotsiopoulos, L. Vaquero Gonzalez, and L. Rodero Merino, “Enhancing Service Selection by Semantic QoS”, L. Aroyo, P. Traverso, F. Ciravegna, P. Cimiano, T. Heath, E. Hyvönen, R. Mizoguchi, E. Oren, M. Sabou, and E. Simperl, editors, The Semantic Web: Research and Applications, volume 5554 of Lecture Notes in Computer Science, pages 565-577, Springer Berlin/Heidelberg, 2009, describe an approach where semantic annotation is used to annotate Service Level Agreement (SLA) templates that are used in automated contract negotiations in service com-position. A service composition QoS ontology is used as a model, and different services advertise their capabilities by using SAWSDL annotations to reference the QoS model.
A framework for translating terminal reports in XML format into RDF class individuals and references between those individuals is disclosed by L. Fallon and D. O'Sullivan, “Using a Semantic Knowledge Base for Communication Service Quality Management in Home Area Networks”, Network Operations and Management Symposium, 2012. NOMS 2012. 13th IEEE/IFIP. NOMS 2012, April 2012. This framework can translate an entire terminal report into RDF individuals in tens of milliseconds.
In order to automate analysis and optimization of end user services, the service expectations, service experience, and service context in which those services are delivered must be managed. As mentioned above, this is a difficult problem because of the variety and volume of services and data on those services that is available. To date, no holistic model for and mechanism for end user service analysis and optimization has been proposed.
As mentioned above, ontologies enable model interoperability, facilitate incremental modelling, are expressively rich, and allow the use of models from various sources. However, initial costs of building models are substantial, semantic content in models varies in detail, and tooling for ontologies is immature as mentioned, for example, in J. Strassner, D. O'Sullivan, and D. Lewis. “Ontologies in the engineering of management and autonomic systems: A reality check”, Journal of Network and Systems Management, 15:5-11, 2007; and Keeney, J. and Boran, A. and Bedini, I. and Matheus, C. J. and Patel-Schneider, P. F, “Approaches to Relating and Integrating Semantic Data from Heterogeneous Sources”, Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011 IEEE/WIC/ACM International Conference on, volume 1, pages 170-177, August. 2011.
There are also performance issues with using ontologies, see for example, Keeney, J. and Boran, A. and Bedini, I. and Matheus, C. J. and Patel-Schneider, P. F, “Approaches to Relating and Integrating Semantic Data from Heterogeneous Sources”, Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011 IEEE/WIC/ACM International Conference on, volume 1, pages 170-177, August. 2011.; and J. López de Vergara, A. Guerrero, V. Villagrá, and J. Berrocal, “Ontology-Based Network Management: Study Cases and Lessons Learned”, Journal of Network and Systems Management, 17:234-254, 2009. Reasoners are slow when used on ontologies with large numbers of instances and large ontologies are expensive to store in memory. Keeney et al. describe the trade-offs in performance between using queries, rules, and reasoning when retrieving knowledge. Typical performance figures for retrieval times vary from 500 ms to 1.5 seconds even for knowledge bases with a small number of individuals.
In summary, existing solutions exhibit the following problems: the amount of services that exist and the amount of data available on those services makes automated optimization of networks to carry those services difficult; there is no holistic model for managing the expectations, experience, and context of end user services; and applying semantic techniques to the problem has promise but those techniques are known to have performance problems.